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Effective intrusion detection is therefore crucial to ensuring the security and resilience of these systems. This paper presents federated learning with feature reduction (Fed-FeRe), a novel approach that enhances decentralized intrusion detection by integrating\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\chi ^{2}$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:msup>\n                            <mml:mi>\u03c7<\/mml:mi>\n                            <mml:mn>2<\/mml:mn>\n                          <\/mml:msup>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    -based feature selection with a gated recurrent unit model. Fed-FeRe introduces an adaptive initialization of the performance threshold\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\alpha $$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mi>\u03b1<\/mml:mi>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    and a data-driven estimation of key hyperparameters (\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\theta _{0}$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:msub>\n                            <mml:mi>\u03b8<\/mml:mi>\n                            <mml:mn>0<\/mml:mn>\n                          <\/mml:msub>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    ,\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\eta $$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mi>\u03b7<\/mml:mi>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    ), enabling robust performance across diverse IoT conditions. By dynamically optimizing feature selection, the framework reduces computational overhead and communication costs, achieving approximately 2% lower transmission costs than FedAvg and 17.6% lower GPU utilization than MOON (Model-Contrastive Federated Learning), while improving detection precision by 3.06%. Fed-FeRe further demonstrates scalability to varying client sizes and adaptability to distinct IoT application domains such as smart cities, healthcare, and industrial networks. These results highlight Fed-FeRe as a scalable, efficient, and privacy-preserving solution for real-world IoT security, advancing the state of federated learning-based intrusion detection.\n                  <\/jats:p>","DOI":"10.1186\/s42400-025-00509-8","type":"journal-article","created":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T06:35:22Z","timestamp":1768372522000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Feature reduction in federated learning for intrusion detection in IoT networks"],"prefix":"10.1186","volume":"9","author":[{"given":"Thien D.","family":"Nguyen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9443-937X","authenticated-orcid":false,"given":"Ammar","family":"Alazab","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ansam","family":"Khraisat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tony","family":"Jan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,14]]},"reference":[{"key":"509_CR1","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.comcom.2022.09.012","volume":"195","author":"S Agrawal","year":"2022","unstructured":"Agrawal S, Sarkar S, Aouedi O, Yenduri G, Piamrat K, Alazab M, Bhattacharya S, Maddikunta PKR, Gadekallu TR (2022) Federated learning for intrusion detection system: concepts, challenges and future directions. 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